System and method for actuator monitoring for an electric aircraft

ABSTRACT

The system and method for actuator monitoring of an electric aircraft is illustrated. The system includes a plurality of flight components, a computing device attached to each rotor component, and a flight controller. The plurality of flight components are coupled to the electric aircraft. A computing device is communicatively coupled to each rotor component and is configured to detect an input health datum associated with each rotor component of the plurality of flight components and transmit the input health datum to a flight controller. The flight controller is communicatively connected to the computing devices and is configured to receive the health datum from the computing devices, generate an updated health datum as a function of the input health datum, and transmit the updated health datum to each rotor component of the plurality of flight components.

FIELD OF THE INVENTION

The present invention generally relates to the field of electric aircrafts. In particular, the present invention is directed to actuator monitoring of an electric aircraft.

BACKGROUND

Having to implement improvements to aircrafts can take away flight time or impact other aspects of the aircraft negatively. Furthermore, failure of an unaddressed issue can result in catastrophic effects.

SUMMARY OF THE DISCLOSURE

In an aspect, the system for actuator monitoring of an electric aircraft is presented. The system comprises a plurality of flight components, computing devices, and a flight controller. The plurality of flight components are coupled to the electric aircraft. A computing device is communicatively coupled to each rotor component and is configured to detect an input health datum associated with each rotor component of the plurality of flight components and transmit the input health datum to a flight controller. The flight controller is communicatively connected to the computing devices and is configured to receive the health datum from the computing devices, generate an updated health datum as a function of the input health datum, and transmit the updated health datum to each rotor component of the plurality of flight components.

In another aspect, the method for actuator monitoring of an electric aircraft is illustrated. The method also includes a plurality of flight components, computing devices, and a flight controller. The method involves coupling a plurality of flight components to an electric aircraft, communicatively coupling a computing device to each flight component, detecting, at the computing device, an input health datum associated with each rotor component of the plurality of flight components, transmitting, at the computing device, the input health datum to a flight controller, communicatively connecting a flight controller to the computing devices, receiving, at the flight controller, the health datum from the computing device, generating, at the flight controller, an updated health datum as a function of the input health datum, and transmitting, by the flight controller, the updated health datum to each rotor component of the plurality of flight components.

These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagrammatic representation of an exemplary embodiment of an electric aircraft;

FIG. 2 is a diagrammatic representation of an exemplary embodiment of a rotor component;

FIG. 3 is a block diagram of an exemplary embodiment of a system for actuator monitoring of an electric aircraft;

FIG. 4 is a block diagram of an exemplary embodiment of a flight controller;

FIG. 5 is a block diagram of an exemplary embodiment of a machine-learning module;

FIG. 6 is a flow diagram of an exemplary embodiment of a method for actuator monitoring of an electric aircraft; and

FIG. 7 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof.

The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. As used herein, the word “exemplary” or “illustrative” means “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other implementations. All of the implementations described below are exemplary implementations provided to enable persons skilled in the art to make or use the embodiments of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. For purposes of description herein, the terms “upper”, “lower”, “left”, “rear”, “right”, “front”, “vertical”, “horizontal”, and derivatives thereof shall relate to the invention as oriented in FIG. 1 . Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed to an aircraft with a visual system. In an embodiment, this disclosure includes an aircraft configured to include a fuselage and a plurality of flight components attached to the fuselage. Aspects of the present disclosure include at least an exterior visual device to provide views of the exterior environment of the aircraft. Aspects of the present disclosure include at least a flight controller configured to receive an input from the visual device and display it for the pilot. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of an aircraft 100 including a system for a visual system is illustrated. In an embodiment, aircraft 100 is an electric aircraft. As used in this disclosure an “aircraft” is any vehicle that may fly by gaining support from the air. As a non-limiting example, aircraft may include airplanes, helicopters, commercial and/or recreational aircrafts, instrument flight aircrafts, drones, electric aircrafts, airliners, rotorcrafts, vertical takeoff and landing aircrafts, jets, airships, blimps, gliders, paramotors, and the like. Aircraft 100 may include an electrically powered aircraft. In embodiments, electrically powered aircraft may be an electric vertical takeoff and landing (eVTOL) aircraft. Electric aircraft may be capable of rotor-based cruising flight, rotor-based takeoff, rotor-based landing, fixed-wing cruising flight, airplane-style takeoff, airplane-style landing, and/or any combination thereof. Electric aircraft may include one or more manned and/or unmanned aircrafts. Electric aircraft may include one or more all-electric short takeoff and landing (eSTOL) aircrafts. For example, and without limitation, eSTOL aircrafts may accelerate plane to a flight speed on takeoff and decelerate plane after landing. In an embodiment, and without limitation, electric aircraft may be configured with an electric propulsion assembly. Electric propulsion assembly may include any electric propulsion assembly as described in U.S. Nonprovisional application Ser. No. 16/603,225, filed on Dec. 4, 2019, and entitled “AN INTEGRATED ELECTRIC PROPULSION ASSEMBLY,” the entirety of which is incorporated herein by reference.

Still referring to FIG. 1 , aircraft 100, may include a fuselage 104, a flight component 108 (or one or more flight parts 108), a plurality of flight components 112, a flight controller 116, and a plurality of computing devices 120. Both the plurality of flight components 112 and computing devices 120 are described further herein with reference to FIGS. 2-3 .

As used in this disclosure, a vertical take-off and landing (VTOL) aircraft is an aircraft that can hover, take off, and land vertically. An eVTOL, as used in this disclosure, is an electrically powered aircraft typically using an energy source, of a plurality of energy sources to power aircraft. To optimize the power and energy necessary to propel aircraft 100, eVTOL may be capable of rotor-based cruising flight, rotor-based takeoff, rotor-based landing, fixed-wing cruising flight, airplane-style takeoff, airplane style landing, and/or any combination thereof. Rotor-based flight, as described herein, is where the aircraft generates lift and propulsion by way of one or more powered rotors or blades coupled with an engine, such as a “quad-copter,” multi-rotor helicopter, or other vehicle that maintains its lift primarily using downward thrusting propulsors. “Fixed-wing flight”, as described herein, is where the aircraft is capable of flight using wings and/or foils that generate lift caused by the aircraft's forward airspeed and the shape of the wings and/or foils, such as airplane-style flight.

Still referring to FIG. 1 , as used in this disclosure a “fuselage” is a main body of an aircraft, or in other words, the entirety of the aircraft except for a cockpit, nose, wings, empennage, nacelles, any and all control surfaces, and generally contains an aircraft's payload. Fuselage 104 may include structural elements that physically support a shape and structure of an aircraft. Structural elements may take a plurality of forms, alone or in combination with other types. Structural elements may vary depending on a construction type of aircraft such as without limitation a fuselage 104. Fuselage 104 may comprise a truss structure. A truss structure may be used with a lightweight aircraft and comprises welded steel tube trusses. A “truss,” as used in this disclosure, is an assembly of beams that create a rigid structure, often in combinations of triangles to create three-dimensional shapes. A truss structure may alternatively comprise wood construction in place of steel tubes, or a combination thereof. In embodiments, structural elements may comprise steel tubes and/or wood beams. In an embodiment, and without limitation, structural elements may include an aircraft skin. Aircraft skin may be layered over the body shape constructed by trusses. Aircraft skin may comprise a plurality of materials such as plywood sheets, aluminum, fiberglass, and/or carbon fiber, the latter of which will be addressed in greater detail later herein.

In embodiments, and with continued reference to FIG. 1 , aircraft fuselage 104 may include and/or be constructed using geodesic construction. Geodesic structural elements may include stringers wound about formers (which may be alternatively called station frames) in opposing spiral directions. A “stringer,” as used in this disclosure, is a general structural element that may include a long, thin, and rigid strip of metal or wood that is mechanically coupled to and spans a distance from, station frame to station frame to create an internal skeleton on which to mechanically couple aircraft skin. A former (or station frame) may include a rigid structural element that is disposed along a length of an interior of aircraft fuselage 104 orthogonal to a longitudinal (nose to tail) axis of the aircraft and may form a general shape of fuselage 104. A former may include differing cross-sectional shapes at differing locations along fuselage 104, as the former is the structural element that informs the overall shape of a fuselage 104 curvature. In embodiments, aircraft skin may be anchored to formers and strings such that the outer mold line of a volume encapsulated by formers and stringers comprises the same shape as aircraft 100 when installed. In other words, former(s) may form a fuselage's ribs, and the stringers may form the interstitials between such ribs. The spiral orientation of stringers about formers may provide uniform robustness at any point on an aircraft fuselage such that if a portion sustains damage, another portion may remain largely unaffected. Aircraft skin may be attached to underlying stringers and formers and may interact with a fluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 1 , fuselage 104 may include and/or be constructed using monocoque construction. Monocoque construction may include a primary structure that forms a shell (or skin in an aircraft's case) and supports physical loads. Monocoque fuselages are fuselages in which the aircraft skin or shell is also the primary structure. In monocoque construction aircraft skin would support tensile and compressive loads within itself and true monocoque aircraft can be further characterized by the absence of internal structural elements. Aircraft skin in this construction method is rigid and can sustain its shape with no structural assistance form underlying skeleton-like elements. Monocoque fuselage may comprise aircraft skin made from plywood layered in varying grain directions, epoxy-impregnated fiberglass, carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 1 , fuselage 104 may include a semi-monocoque construction. Semi-monocoque construction, as used herein, is a partial monocoque construction, wherein a monocoque construction is describe above detail. In semi-monocoque construction, aircraft fuselage 104 may derive some structural support from stressed aircraft skin and some structural support from underlying frame structure made of structural elements. Formers or station frames can be seen running transverse to the long axis of fuselage 104 with circular cutouts which are generally used in real-world manufacturing for weight savings and for the routing of electrical harnesses and other modern on-board systems. In a semi-monocoque construction, stringers are thin, long strips of material that run parallel to fuselage's long axis. Stringers may be mechanically coupled to formers permanently, such as with rivets. Aircraft skin may be mechanically coupled to stringers and formers permanently, such as by rivets as well. A person of ordinary skill in the art will appreciate, upon reviewing the entirety of this disclosure, that there are numerous methods for mechanical fastening of components like screws, nails, dowels, pins, anchors, adhesives like glue or epoxy, or bolts and nuts, to name a few. A subset of fuselage under the umbrella of semi-monocoque construction includes unibody vehicles. Unibody, which is short for “unitized body” or alternatively “unitary construction”, vehicles are characterized by a construction in which the body, floor plan, and chassis form a single structure. In the aircraft world, unibody may be characterized by internal structural elements like formers and stringers being constructed in one piece, integral to the aircraft skin as well as any floor construction like a deck.

Still referring to FIG. 1 , stringers and formers, which may account for the bulk of an aircraft structure excluding monocoque construction, may be arranged in a plurality of orientations depending on aircraft operation and materials. Stringers may be arranged to carry axial (tensile or compressive), shear, bending or torsion forces throughout their overall structure. Due to their coupling to aircraft skin, aerodynamic forces exerted on aircraft skin will be transferred to stringers. A location of said stringers greatly informs the type of forces and loads applied to each and every stringer, all of which may be handled by material selection, cross-sectional area, and mechanical coupling methods of each member. A similar assessment may be made for formers. In general, formers may be significantly larger in cross-sectional area and thickness, depending on location, than stringers. Both stringers and formers may comprise aluminum, aluminum alloys, graphite epoxy composite, steel alloys, titanium, or an undisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 1 , stressed skin, when used in semi-monocoque construction is the concept where the skin of an aircraft bears partial, yet significant, load in an overall structural hierarchy. In other words, an internal structure, whether it be a frame of welded tubes, formers and stringers, or some combination, may not be sufficiently strong enough by design to bear all loads. The concept of stressed skin may be applied in monocoque and semi-monocoque construction methods of fuselage 104. Monocoque comprises only structural skin, and in that sense, aircraft skin undergoes stress by applied aerodynamic fluids imparted by the fluid. Stress as used in continuum mechanics may be described in pound-force per square inch (lbf/in²) or Pascals (Pa). In semi-monocoque construction stressed skin may bear part of aerodynamic loads and additionally may impart force on an underlying structure of stringers and formers.

Still referring to FIG. 1 , it should be noted that an illustrative embodiment is presented only, and this disclosure in no way limits the form or construction method of a system and method for loading payload into an eVTOL aircraft. In embodiments, fuselage 104 may be configurable based on the needs of the eVTOL per specific mission or objective. The general arrangement of components, structural elements, and hardware associated with storing and/or moving a payload may be added or removed from fuselage 104 as needed, whether it is stowed manually, automatedly, or removed by personnel altogether. Fuselage 104 may be configurable for a plurality of storage options. Bulkheads and dividers may be installed and uninstalled as needed, as well as longitudinal dividers where necessary. Bulkheads and dividers may be installed using integrated slots and hooks, tabs, boss and channel, or hardware like bolts, nuts, screws, nails, clips, pins, and/or dowels, to name a few. Fuselage 104 may also be configurable to accept certain specific cargo containers, or a receptable that can, in turn, accept certain cargo containers.

Still referring to FIG. 1 , aircraft 100 may include a plurality of laterally extending elements attached to fuselage 104. As used in this disclosure a “laterally extending element” is an element that projects essentially horizontally from fuselage, including an outrigger, a spar, and/or a fixed wing that extends from fuselage. Wings may be structures which may include airfoils configured to create a pressure differential resulting in lift. Wings may generally dispose on the left and right sides of the aircraft symmetrically, at a point between nose and empennage. Wings may comprise a plurality of geometries in planform view, swept swing, tapered, variable wing, triangular, oblong, elliptical, square, among others. A wing's cross section geometry may comprise an airfoil. An “airfoil” as used in this disclosure is a shape specifically designed such that a fluid flowing above and below it exert differing levels of pressure against the top and bottom surface. In embodiments, the bottom surface of an aircraft can be configured to generate a greater pressure than does the top, resulting in lift. Laterally extending element may comprise differing and/or similar cross-sectional geometries over its cord length or the length from wing tip to where wing meets aircraft's body. One or more wings may be symmetrical about aircraft's longitudinal plane, which comprises the longitudinal or roll axis reaching down the center of aircraft through the nose and empennage, and plane's yaw axis. Laterally extending element may comprise controls surfaces configured to be commanded by a pilot or pilots to change a wing's geometry and therefore its interaction with a fluid medium, like air. Control surfaces may comprise flaps, ailerons, tabs, spoilers, and slats, among others. The control surfaces may dispose on the wings in a plurality of locations and arrangements and in embodiments may be disposed at the leading and trailing edges of the wings, and may be configured to deflect up, down, forward, aft, or a combination thereof. An aircraft, including a dual-mode aircraft may comprise a combination of control surfaces to perform maneuvers while flying or on ground.

Still referring to FIG. 1 , aircraft 100 may include a plurality of flight components 108. As used in this disclosure a “flight component” is a component that promotes flight and guidance of an aircraft. In an embodiment, flight component 108 may be mechanically coupled to an aircraft. As used herein, a person of ordinary skill in the art would understand “mechanically coupled” to mean that at least a portion of a device, component, or circuit is connected to at least a portion of the aircraft via a mechanical coupling. Said mechanical coupling may include, for example, rigid coupling, such as beam coupling, bellows coupling, bushed pin coupling, constant velocity, split-muff coupling, diaphragm coupling, disc coupling, donut coupling, elastic coupling, flexible coupling, fluid coupling, gear coupling, grid coupling, hirth joints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldham coupling, sleeve coupling, tapered shaft lock, twin spring coupling, rag joint coupling, universal joints, or any combination thereof. In an embodiment, mechanical coupling may be used to connect the ends of adjacent parts and/or objects of an electric aircraft. Further, in an embodiment, mechanical coupling may be used to join two pieces of rotating electric aircraft components.

Still referring to FIG. 1 , plurality of flight components 108 may include at least a lift propulsor. As used in this disclosure a “propulsor” is a component and/or device used to propel a craft upward by exerting force on a fluid medium, which may include a gaseous medium such as air or a liquid medium such as water. Propulsor may include any device or component that consumes electrical power on demand to propel an electric aircraft in a direction or other vehicle while on ground or in-flight. For example, and without limitation, propulsor may include a rotor, propeller, paddle wheel and the like thereof. In an embodiment, propulsor may include a plurality of blades. As used in this disclosure a “blade” is a propeller that converts rotary motion from an engine or other power source into a swirling slipstream. In an embodiment, blade may convert rotary motion to push the propeller forwards or backwards. In an embodiment propulsor may include a rotating power-driven hub, to which are attached several radial airfoil-section blades such that the whole assembly rotates about a longitudinal axis. The lift propulsor is further described herein with reference to FIG. 2 .

In an embodiment, and still referring to FIG. 1 , plurality of flight components 108 may include one or more power sources. As used in this disclosure a “power source” is a source that that drives and/or controls any other flight component. For example, and without limitation power source may include a motor that operates to move one or more lift propulsor components, to drive one or more blades, or the like thereof. A motor may be driven by direct current (DC) electric power and may include, without limitation, brushless DC electric motors, switched reluctance motors, induction motors, or any combination thereof. A motor may also include electronic speed controllers or other components for regulating motor speed, rotation direction, and/or dynamic braking. In an embodiment, power source may include an inverter. As used in this disclosure an “inverter” is a device that changes one or more currents of a system. For example, and without limitation, inverter may include one or more electronic devices that change direct current to alternating current. As a further non-limiting example, inverter may include receiving a first input voltage and outputting a second voltage, wherein the second voltage is different from the first voltage. In an embodiment, and without limitation, inverter may output a waveform, wherein a waveform may comprise a square wave, sine wave, modified sine wave, near sine wave, and the like thereof.

Still referring to FIG. 1 , power source may include an energy source. An energy source may include, for example, a generator, a photovoltaic device, a fuel cell such as a hydrogen fuel cell, direct methanol fuel cell, and/or solid oxide fuel cell, an electric energy storage device (e.g. a capacitor, an inductor, and/or a battery). An energy source may also include a battery cell, or a plurality of battery cells connected in series into a module and each module connected in series or in parallel with other modules. Configuration of an energy source containing connected modules may be designed to meet an energy or power requirement and may be designed to fit within a designated footprint in an electric aircraft in which aircraft 100 may be incorporated.

In an embodiment, and still referring to FIG. 1 , an energy source may be used to provide a steady supply of electrical power to a load over the course of a flight by a vehicle or other electric aircraft. For example, the energy source may be capable of providing sufficient power for “cruising” and other relatively low-energy phases of flight. An energy source may also be capable of providing electrical power for some higher-power phases of flight as well, particularly when the energy source is at a high SOC, as may be the case for instance during takeoff. In an embodiment, the energy source may be capable of providing sufficient electrical power for auxiliary loads including without limitation, lighting, navigation, communications, de-icing, steering or other systems requiring power or energy. Further, the energy source may be capable of providing sufficient power for controlled descent and landing protocols, including, without limitation, hovering descent or runway landing. As used herein the energy source may have high power density where the electrical power an energy source can usefully produce per unit of volume and/or mass is relatively high. The electrical power is defined as the rate of electrical energy per unit time. An energy source may include a device for which power that may be produced per unit of volume and/or mass has been optimized, at the expense of the maximal total specific energy density or power capacity, during design. Non-limiting examples of items that may be used as at least an energy source may include batteries used for starting applications including Li ion batteries which may include NCA, NMC, Lithium iron phosphate (LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may be mixed with another cathode chemistry to provide more specific power if the application requires Li metal batteries, which have a lithium metal anode that provides high power on demand, Li ion batteries that have a silicon or titanite anode, energy source may be used, in an embodiment, to provide electrical power to an electric aircraft or drone, such as an electric aircraft vehicle, during moments requiring high rates of power output, including without limitation takeoff, landing, thermal de-icing and situations requiring greater power output for reasons of stability, such as high turbulence situations, as described in further detail below. A battery may include, without limitation a battery using nickel based chemistries such as nickel cadmium or nickel metal hydride, a battery using lithium ion battery chemistries such as a nickel cobalt aluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide (LMO), a battery using lithium polymer technology, lead-based batteries such as without limitation lead acid batteries, metal-air batteries, or any other suitable battery. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various devices of components that may be used as an energy source.

Still referring to FIG. 1 , an energy source may include a plurality of energy sources, referred to herein as a module of energy sources. The module may include batteries connected in parallel or in series or a plurality of modules connected either in series or in parallel designed to deliver both the power and energy requirements of the application. Connecting batteries in series may increase the voltage of at least an energy source which may provide more power on demand. High voltage batteries may require cell matching when high peak load is needed. As more cells are connected in strings, there may exist the possibility of one cell failing which may increase resistance in the module and reduce the overall power output as the voltage of the module may decrease as a result of that failing cell. Connecting batteries in parallel may increase total current capacity by decreasing total resistance, and it also may increase overall amp-hour capacity. The overall energy and power outputs of at least an energy source may be based on the individual battery cell performance or an extrapolation based on the measurement of at least an electrical parameter. In an embodiment where the energy source includes a plurality of battery cells, the overall power output capacity may be dependent on the electrical parameters of each individual cell. If one cell experiences high self-discharge during demand, power drawn from at least an energy source may be decreased to avoid damage to the weakest cell. The energy source may further include, without limitation, wiring, conduit, housing, cooling system and battery management system. Persons skilled in the art will be aware, after reviewing the entirety of this disclosure, of many different components of an energy source.

Still referring to FIG. 1 , plurality of flight components 108 may include a pusher component. As used in this disclosure a “pusher component” is a component that pushes and/or thrusts an aircraft through a medium. As a non-limiting example, pusher component may include a pusher propeller, a paddle wheel, a pusher motor, a pusher propulsor, and the like. Additionally, or alternatively, pusher flight component may include a plurality of pusher flight components. Pusher component may be configured to produce a forward thrust. As used in this disclosure a “forward thrust” is a thrust that forces aircraft through a medium in a horizontal direction, wherein a horizontal direction is a direction parallel to the longitudinal axis. For example, forward thrust may include a force of 1145 N to force aircraft to in a horizontal direction along the longitudinal axis. As a further non-limiting example, pusher component may twist and/or rotate to pull air behind it and, at the same time, push aircraft 100 forward with an equal amount of force. In an embodiment, and without limitation, the more air forced behind aircraft, the greater the thrust force with which aircraft 100 is pushed horizontally will be. In another embodiment, and without limitation, forward thrust may force aircraft 100 through the medium of relative air. Additionally or alternatively, plurality of flight components 108 may include one or more puller components. As used in this disclosure a “puller component” is a component that pulls and/or tows an aircraft through a medium. As a non-limiting example, puller component may include a flight component such as a puller propeller, a puller motor, a tractor propeller, a puller propulsor, and the like. Additionally, or alternatively, puller component may include a plurality of puller flight components.

Still referring to FIG. 1 , aircraft 100 may have flight controller 112 attached. As used in this disclosure a “flight controller” is a computing device of a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller 112 may include and/or communicate with any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Further, flight controller 112 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. In embodiments, flight controller 112 may be installed in an aircraft, may control the aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith. In an embodiment, and without limitation, flight controller 112 may be configured to command a plurality of flight components, wherein flight components are described in reference to FIG. 1 . Flight controller is described herein more detail in reference to FIG. 3 .

Referring now to FIG. 2 , an exemplary embodiment of an electric rotor and propulsion system 200 is illustrated. Electric rotor and propulsion system 200 is an example of a possible flight component to which the monitoring system will monitor. Rotor system 200 includes at least a stator 204. Stator 204, as used herein, is a stationary component of a motor and/or motor assembly. In an embodiment, stator 204 may include at least a first magnetic element 208. As used herein, first magnetic element 208 is an element that generates a magnetic field. For example, first magnetic element 208 may include one or more magnets which may be assembled in rows along a structural casing component. Further, first magnetic element 208 may include one or more magnets having magnetic poles oriented in at least a first direction. The magnets may include at least a permanent magnet. Permanent magnets may be composed of, but are not limited to, ceramic, alnico, samarium cobalt, neodymium iron boron materials, any rare earth magnets, and the like. Further, the magnets may include an electromagnet. As used herein, an electromagnet is an electrical component that generates magnetic field via induction; the electromagnet may include a coil of electrically conducting material, through which an electric current flow to generate the magnetic field, also called a field coil of field winding. A coil may be wound around a magnetic core, which may include without limitation an iron core or other magnetic material. The core may include a plurality of steel rings insulated from one another and then laminated together; the steel rings may include slots in which the conducting wire will wrap around to form a coil. A first magnetic element 208 may act to produce or generate a magnetic field to cause other magnetic elements to rotate, as described in further detail below. Stator 204 may include a frame to house components including at least a first magnetic element 208, as well as one or more other elements or components as described in further detail below. In an embodiment, a magnetic field can be generated by a first magnetic element 208 and can comprise a variable magnetic field. In embodiments, a variable magnetic field may be achieved by use of an inverter, a controller, or the like. In an embodiment, stator 204 may have an inner and outer cylindrical surface; a plurality of magnetic poles may extend outward from the outer cylindrical surface of the stator. In an embodiment, stator 204 may include an annular stator, wherein the stator is ring-shaped. In an embodiment, stator 204 is incorporated into a DC motor where stator 204 is fixed and functions to supply the magnetic fields where a corresponding rotor, as described in further detail below, rotates. In an embodiment, stator 204 is incorporated an AC motor where stator 204 is fixed and functions to supply the magnetic fields by radio frequency electric currents through an electromagnet to a corresponding rotor, as described in further detail below, rotates.

Still referring to FIG. 2 , rotor system 200 includes propulsor 212. In embodiments, propulsor 212 can include an integrated rotor. As used herein, a rotor is a portion of an electric motor that rotates with respect to a stator of the electric motor, such as stator 204. A propulsor, as used herein, is a component or device used to propel a craft by exerting force on a fluid medium, which may include a gaseous medium such as air or a liquid medium such as water. Propulsor 212 may be any device or component that consumes electrical power on demand to propel an aircraft or other vehicle while on ground and/or in flight. Propulsor 212 may include one or more propulsive devices. In an embodiment, propulsor 212 can include a thrust element which may be integrated into the propulsor. A thrust element may include any device or component that converts the mechanical energy of a motor, for instance in the form of rotational motion of a shaft, into thrust in a fluid medium. For example, a thrust element may include without limitation a marine propeller or screw, an impeller, a turbine, a pump-jet, a paddle or paddle-based device, or the like. As another non-limiting example, at least a propulsor may include an eight-bladed pusher propeller, such as an eight-bladed propeller mounted behind the engine to ensure the drive shaft is in compression. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various devices that may be used as at least a thrust element. As used herein, a propulsive device may include, without limitation, a device using moving or rotating foils, including without limitation one or more rotors, an airscrew or propeller, a set of airscrews or propellers such as contra-rotating propellers, a moving or flapping wing, or the like.

In an embodiment, propulsor 212 may include at least a blade. As another non-limiting example, a propulsor may include an eight-bladed pusher propeller, such as an eight-bladed propeller mounted behind the engine to ensure the drive shaft is in compression. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various devices that may be used as propulsor 212. In an embodiment, when a propulsor twists and pulls air behind it, it will, at the same time, push the aircraft forward with an equal amount of force. The more air pulled behind the aircraft, the more the aircraft is pushed forward. In an embodiment, thrust element may include a helicopter rotor incorporated into propulsor 212. A helicopter rotor may include one or more blade or wing elements driven in a rotary motion to drive fluid medium in a direction axial to the rotation of the blade or wing element. Its rotation is due to the interaction between the windings and magnetic fields which produces a torque around the rotor's axis. A helicopter rotor may include a plurality of blade or wing elements.

Continuing to refer to FIG. 2 , propulsor 212 can include a hub 216 rotatably mounted to stator 204. Rotatably mounted, as described herein, is functionally secured in a manner to allow rotation. Hub 216 is a structure which allows for the mechanically coupling of components of the integrated rotor assembly. In an embodiment, hub 216 can be mechanically coupled to propellers or blades. In an embodiment, hub 216 may be cylindrical in shape such that it may be mechanically joined to other components of the rotor assembly. Hub 216 may be constructed of any suitable material or combination of materials, including without limitation metal such as aluminum, titanium, steel, or the like, polymer materials or composites, fiberglass, carbon fiber, wood, or any other suitable material. Hub 216 may move in a rotational manner driven by interaction between stator and components in the rotor assembly. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various structures that may be used as or included as hub 116, as used and described herein.

Still referring to FIG. 2 , propulsor 212 and/or rotor shaft 236 can include a second magnetic element 220, which may include one or more further magnetic elements. Second magnetic element 220 generates a magnetic field designed to interact with first magnetic element 208. Second magnetic element 220 may be designed with a material such that the magnetic poles of at least a second magnetic element are oriented in an opposite direction from first magnetic element 208. In an embodiment, second magnetic element 220 may be affixed to hub 216, rotor shaft 236, or another rotating or stationary electric motor component disclosed herein. Affixed, as described herein, is the attachment, fastening, connection, and the like, of one component to another component. For example, and without limitation, affixed may include bonding the second magnetic element 220 to hub 216, such as through hardware assembly, spot welding, riveting, brazing, soldering, glue, and the like. Second magnetic element 220 may include any magnetic element suitable for use as a first magnetic element 208. For instance, and without limitation, second magnetic element may include a permanent magnet and/or an electromagnet. Second magnetic element 220 may include magnetic poles oriented in a second direction opposite, in whole or in part, of the orientation of the poles of first magnetic element 208. In an embodiment, rotor system 200 includes a motor assembly incorporating stator 204 with a first magnet element and second magnetic element 220. First magnetic element 208 includes magnetic poles oriented in a first direction, a second magnetic element includes a plurality of magnetic poles oriented in the opposite direction than the plurality of magnetic poles in the first magnetic element 208.

Referring again to FIG. 2 , first magnetic element 208 may be a productive element, defined herein as an element that produces a varying magnetic field. Productive elements will produce magnetic field that will attract and other magnetic elements, including a receptive element. Second magnetic element may be a productive or receptive element. A receptive element will react due to the magnetic field of a first magnetic element 208. In an embodiment, first magnetic element 208 produces a magnetic field according to magnetic poles of first magnetic element 208 oriented in a first direction. Second magnetic element 220 may produce a magnetic field with magnetic poles in the opposite direction of the first magnetic field, which may cause the two magnetic elements to attract one another. Receptive magnetic element may be slightly larger in diameter than the productive element. Interaction of productive and receptive magnetic elements may produce torque and cause the assembly to rotate. Hub 216 and rotor assembly may both be cylindrical in shape where rotor may have a slightly smaller circumference than hub 216 to allow the joining of both structures. Coupling of hub 216 to stator 204 may be accomplished via a surface modification of either hub 216, stator 204 or both to form a locking mechanism. Coupling may be accomplished using additional nuts, bolts, and/or other fastening apparatuses. In an embodiment, an integrated rotor assembly as described above reduces profile drag in forward flight for an electric aircraft. Profile drag may be caused by a number of external forces that the aircraft is subjected to. By incorporating a propulsor 212 into hub 216, a profile of rotor system 200 may be reduced, resulting in a reduced profile drag, as noted above. In an embodiment, the rotor, which includes motor inner magnet carrier 224, motor outer magnet carrier 228, propulsor 212 is incorporated into hub 216 to become one integrated unit. In an embodiment, inner motor magnet carrier 212 rotates in response to a magnetic field. The rotation causes hub 216 to rotate. This unit can be inserted into rotor system 200 as one unit. This enables ease of installation, maintenance, and removal.

Still referring to FIG. 2 , stator 204 may include a through-hole 232. Through-hole 232 may provide an opening for a component to be inserted through to aid in attaching propulsor with integrated rotor and rotor shaft to stator. In an embodiment, through-hole 232 may have a round or cylindrical shape and be located at a rotational axis of stator 204. Hub 216 may be mounted to stator 204 by means of a rotor shaft 136 rotatably inserted though through hole 232. The rotor shaft 236 may be mechanically coupled to stator 204 such that rotor shaft 236 is free to rotate about its centerline axis, which may be effectively parallel and coincident to stator's centerline axis, and further the rotor shaft and stator may comprise a void of empty space between them, where at least a portion the outer cylindrical surface of the rotor shaft is not physically contacting at least a portion of the inner cylindrical surface of the stator. This void may be filled, in whole or in part, by air, a vacuum, a partial vacuum or other gas or combination of gaseous elements and/or compounds, to name a few. Through-hole 232 may have a diameter that is slightly larger than a diameter of rotor shaft 236 to allow rotor shaft 236 to fit through through-hole 232 to connect stator 204 to hub 216. Rotor shaft 236 may rotate in response to rotation of propulsor 212.

Still referring to FIG. 2 , rotor system 200 may include a bearing cartridge 240. Bearing cartridge 240 may include a bore. Rotor shaft 236 may be inserted through the bore of bearing cartridge 240. Bearing cartridge 240 may be attached to a structural element of a vehicle. Bearing cartridge 240 functions to support the rotor and to transfer the loads from the motor. Loads may include, without limitation, weight, power, magnetic pull, pitch errors, out of balance situations, and the like. A bearing cartridge 240 may include a bore. A bearing cartridge 240 may include a smooth metal ball or roller that rolls against a smooth inner and outer metal surface. The rollers or balls take the load, allowing the device to spin. a bearing may include, without limitation, a ball bearing, a straight roller bearing, a tapered roller bearing or the like. a bearing cartridge 240 may be subject to a load which may include, without limitation, a radial or a thrust load. Depending on the location of bearing cartridge 240 in the assembly, it may see all of a radial or thrust load or a combination of both. In an embodiment, bearing cartridge 240 may join rotor system 200 to a structure feature. A bearing cartridge 240 may function to minimize the structural impact from the transfer of bearing loads during flight and/or to increase energy efficiency and power of propulsor. A bearing cartridge 240 may include a shaft and collar arrangement, wherein a shaft affixed into a collar assembly. A bearing element may support the two joined structures by reducing transmission of vibration from such bearings. Roller (rolling-contact) bearings are conventionally used for locating and supporting machine parts such as rotors or rotating shafts. Typically, the rolling elements of a roller bearing are balls or rollers. In general, a roller bearing is a is type of anti-friction bearing; a roller bearing functions to reduce friction allowing free rotation. Also, a roller bearing may act to transfer loads between rotating and stationary members. In an embodiment, bearing cartridge 240 may act to keep a propulsor 212 and components intact during flight by allowing rotor system 200 to rotate freely while resisting loads such as an axial force. In an embodiment, bearing cartridge 240 includes a roller bearing incorporated into the bore. A roller bearing is in contact with rotor shaft 236. Stator 204 is mechanically coupled to inverter housing 240. Mechanically coupled may include a mechanical fastening, without limitation, such as nuts, bolts or other fastening device. Mechanically coupled may include welding or casting or the like. Inverter housing contains a bore which allows insertion by rotor shaft 236 into bearing cartridge 240.

Still referring to FIG. 2 , electric propulsion assembly 200 may include a motor assembly incorporating a rotating assembly and a stationary assembly. Hub 216, motor inner magnet carrier 224 and rotor shaft 236 may be incorporated into the rotor assembly of rotor system 200 which make up rotating parts of electric motor, moving between the stator poles and transmitting the motor power. As one integrated part, the rotor assembly may be inserted and removed in one piece. Stator 204 may be incorporated into the stationary part of the motor assembly. Stator and rotor may combine to form an electric motor. In embodiment, an electric motor may, for instance, incorporate coils of wire which are driven by the magnetic force exerted by a first magnetic field on an electric current. The function of the motor may be to convert electrical energy into mechanical energy. In operation, a wire carrying current may create at least a first magnetic field with magnetic poles in a first orientation which interacts with a second magnetic field with magnetic poles oriented in the opposite direction of the first magnetic pole direction causing a force that may move a rotor in a direction. For example, and without limitation, a first magnetic element 208 in rotor system 200 may include an active magnet. For instance, and without limitation, a second magnetic element may include a passive magnet, a magnet that reacts to a magnetic force generated by a first magnetic element 208. In an embodiment, a first magnet positioned around the rotor assembly, may generate magnetic fields to affect the position of the rotor relative to the stator 204. A controller may have an ability to adjust electricity originating from a power supply and, thereby, the magnetic forces generated, to ensure stable rotation of the rotor, independent of the forces induced by the machinery process.

Rotor system 200 may include an impeller 244 coupled with the rotor shaft 236. An impeller, as described herein, is a rotor used to increase or decrease the pressure and flow of a fluid, including at least air. Impeller 244 may function to provide cooling to rotor system 200. Impeller 244 may include varying blade configurations, such as radial blades, non-radial blades, semi-circular blades and airfoil blades. Impeller 214 may further include single and/or double-sided configurations. Impeller 214 is described in further detail below. Additionally, or alternatively, in a non-limiting illustrative example, rotor shaft 236 may be mechanically coupled to cooling vanes. Cooling vanes are used to lower the temperature of a high-velocity mechanical part, like the rotor in an electrical motor. Cooling vanes may employ a plurality of physical principles to cool mechanical parts. Cooling vanes may draw cool air like a fan if mechanically coupled to the rotor at an angle sufficient to create a pressure differential in order to draw cool air from outside the motor housing into the relatively hot inner motor and cool internal mechanical parts by convection. Convection cooling in principle, is cooling of a portion of a body by moving a fluid over it, the tendency of heat energy to move from high to low energy areas, like a hot spinning rotor to cool moving air. Additionally, cooling vanes may act as thermodynamic fins. Heat energy may be conducted through the cooling vanes from the hot rotor shaft to the tips of the cooling vanes, thus dissipating heat in a high-speed rotating part.

Now referring to FIG. 3 , system 300 illustrates an exemplary embodiment of an actuator monitoring system of an electric aircraft. System 300 includes rotor component 112, computing device 120, flight controller 116, input health datum 304, updated health datum 308, and/or any combination thereof.

Referring to FIG. 3 , a computing device 120 is attached to each individual rotor component of the plurality of flight components 112. In this disclosure, a “computing device” is any electronic equipment controlled by a CPU and may include a computer, tablet, smartphone, or the like. Computing device may include a flight controller. As used in this disclosure a “flight controller” is a computing device of a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller may include and/or communicate with any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Further, flight controller may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. In embodiments, flight controller may be installed in an aircraft, may control the aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith. In an embodiment, and without limitation, flight controller may be configured to command a plurality of flight components. Computing device 120 is described further herein with reference to FIG. 7 .

Still referring to FIG. 3 , computing device 120 detects input health datum 304. In this disclosure, “input health datum” is a piece of data concerned with the wellness of a specific component of the rotor. For example, but without limitation, the health datum could be concerned with battery health, motor health, or the health of any of component as foresaid in FIG. 2 . Examples of health datums include but are not limited to “poor battery health”, “too high motor temperature”, “propulsor blade broken”, or the like. The term “health” refers to how well or healthy a component is; for example but without limitation, the computing device 120 may detect temperature, rotational speed, missing parts, age, efficiency, voltage, or the like. In this disclosure, input health datum is configured to include a plurality of battery temperatures. Additionally, heath datum may be qualitative or quantitative, such as that something is in “good” health or “poor” health. In order to detect such health datums, each rotor component of the plurality of flight components 112 may further comprise a sensor. The sensor is configured to detect an input health datum 304 and transmit the datum to the computing device. For example, the sensor may be configured to detect a battery metric from each battery and transmit the battery metric to the computing device 120 and then to the flight controller 116. As used in this disclosure a “sensor” is a device, module, and/or subsystem, utilizing any hardware, software, and/or any combination thereof to detect events and/or changes in the instant environment and transmit the information. Sensor may be attached via a mechanically and/or communicatively coupled, as described above, to aircraft. For example, and without limitation, sensor may include a potentiometric sensor, inductive sensor, capacitive sensor, piezoelectric sensor, strain gauge sensor, variable reluctance sensor, and the like thereof. Sensor may include one or more environmental sensors, which may function to sense parameters of the environment surrounding the aircraft. An environmental sensor may include without limitation one or more sensors used to detect ambient temperature, barometric pressure, and/or air velocity, one or more motion sensors which may include without limitation gyroscopes, accelerometers, inertial measurement unit (IMU), and/or magnetic sensors, one or more humidity sensors, one or more oxygen sensors, or the like. Additionally or alternatively, sensor may include a geospatial sensor. Sensor may be located inside aircraft; and/or be included in and/or attached to at least a portion of the aircraft. Sensor may include one or more proximity sensors, displacement sensors, vibration sensors, and the like thereof. Sensor may be used to monitor the status of aircraft for both critical and non-critical functions. Sensor may be incorporated into vehicle or aircraft or be remote. Sensor may be communicatively connected to an energy source.

Still referring to FIG. 3 , at least sensor may further include a sensor suite, the sensor suite including a plurality of individual sensors. At least a sensor is configured to detect aircraft's position and rate. At least a sensor may further include circuitry or electronic components configured to digitize, transform, or otherwise manipulate electrical signals. Electrical signals may include analog signals, digital signals, periodic or aperiodic signal, step signals, unit impulse signal, unit ramp signal, unit parabolic signal, signum function, exponential signal, rectangular signal, triangular signal, sinusoidal signal, sinc function, or pulse width modulated signal. At least a sensor may include, but not limited to, a pressure sensor, proximity sensor, light sensor, pitot tubes, air speed sensors, and the like. At least a sensor may include a field camera. “Field camera,” for the purposes of this disclosure, is an optical device or combination of optical devices, configured to capture a field of vision as an electrical signal, to form a digital image. Field camera may include a single camera and/or two or more cameras used to capture field of vision. In a non-limiting embodiment, two or more cameras may capture two or more perspectives for use in stereoscopic and/or three-dimensional display, as described below. Field camera may capture a feed including a plurality of frames, such as without limitation a video feed.

With continued reference to FIG. 3 , at least sensor may include at least a motion sensor. At least a motion sensor may include, without limitation, a microelectromechanical system (MEMS) sensor. “Motion sensor”, for the purposes of this disclosure, is a device or component configured to detect physical movement of an object or grouping of objects. One of ordinary skill in the art would appreciate, after reviewing the entirety of this disclosure, that motion may include a plurality of types including but not limited to: spinning, rotating, oscillating, gyrating, jumping, sliding, reciprocating, and the like. At least a motion sensor may include, without limitation, an inertial measurement unit (IMU). At least a motion sensor may include one or more accelerometers; one or more accelerometers may include a plurality of accelerometers, such as three or more accelerometers positioned to span three dimensions of possible acceleration, so that any direction and magnitude of acceleration in three dimensions may be detected and measured in three dimensions. At least a motion sensor may include one or more gyroscopes; one or more gyroscopes may include a plurality of gyroscopes, such as three or more gyroscopes positioned to span three dimensions of possible acceleration, so that any direction and magnitude of change in angular position in three dimensions may be detected and measured in three dimensions. At least a motion sensor may include, without limitation magnetic sensors such as Hall effect sensors, compasses such as solid-state compasses, or the like. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various components and/or devices that may be used as at least a motion sensor consistently with this disclosure.

With continued reference to FIG. 3 , at least sensor may include a three-dimensional (3D) scanner. 4D scanner may include the use of 4D laser scanning. 4D Laser Scanning is a non-contact, non-destructive technology that digitally captures the shape of physical objects using a line of laser light. 4D laser scanners create “point clouds” of data from the surface of an object. In other words, 4D laser scanning is a way to capture a physical object's exact size and shape into the computer world as a digital 4-dimensional representation. 4D laser scanners measure fine details and capture free-form shapes to quickly generate highly accurate point clouds. 4D laser scanning is ideally suited to the measurement and inspection of contoured surfaces and complex geometries which require massive amounts of data for their accurate description and where doing this is impractical with the use of traditional measurement methods or a touch probe. In a non-limiting embodiment, a 4D scanner may capture a potential landing zone and generate a 4D model of a plot representing the landing zone for analysis described later in the disclosure.

With continued reference to FIG. 3 , at least sensor may be configured to detect and/or determine a plurality of ranges of an object with a laser. Determining ranges may include a technique for the measuring of distances or slant range from an observer including at least a sensor to a target. Technique may include the use of active range finding methods which may include, but not limited to, light detection and ranging (LIDAR), radar, sonar, ultrasonic range finding, and the like. In a non-limiting embodiment, at least sensor may include at least a LIDAR system to measure ranges including variable distances from at least the sensor. LIDAR systems may include, but not limited to, a laser, at least a phased array, at least a microelectromechanical machine, at least a scanner and/or optic, a photosensor, a specialized GPS receiver, and the like. In a non-limiting embodiment, at least sensor including a LIDAR system may target an object with a laser and measure the time for at least a reflected light to return to the LIDAR system. LIDAR may also be used to make digital 4-D representations of areas on the earth's surface and ocean bottom, due to differences in laser return times, and by varying laser wavelengths. In a non-limiting embodiment the LIDAR system may include a topographic LIDAR and a bathymetric LIDAR, wherein the topographic LIDAR that may use near-infrared laser to map a plot of a land or surface while the bathymetric LIDAR may use water-penetrating green light to measure seafloor and various water level elevations. In a non-limiting embodiment, electric aircraft may use at least a LIDAR system as a means of obstacle detection and avoidance to navigate safely through environments.

Still referring to FIG. 3 , input health datum 304 is transmitted to flight controller 116 by the computing device 120. Flight controller 116 is further described herein with reference to FIG. 4 and computing device 120 is further described herein with reference to FIG. 7 .

Still referring to FIG. 3 , flight controller 116 generates an updates health datum 308. Updated health datum is very similar to the input health datum; however the updated health datum is generated by the flight controller and uses a threshold to make the rotor component “healthier”. In this disclosure, “updated health datum” is a piece of data concerned with the wellness of the specific component of the rotor that sent an input health datum. “Updated health datum”, as used in this disclosure, is any data wherein the flight controller uses the information stored inside it to create a new health datum that is closer to the threshold, or ideal datum. Updated health datum is configured to include a plurality of battery temperature in reference to a threshold temperature. Examples of threshold temperatures include but are not limited to 300 K, 50 degrees Fahrenheit, 30 degrees Celsius, “high” temperature, and the like.

Still referring to FIG. 3 , flight controller 116 transmits updates health datum 308 back to plurality of flight components 112 to be implemented. The plurality of flight components are configured to receive the updated health datum and adjust the component to reflect the datum.

Still referring to FIG. 3 , system 300 may also include an input device configured to detect at least a command and transmit that command to the flight controller 116. In this disclosure, “input device” is any device that receives and sends data. Examples of input devices that are relevant to this disclosure, but not limited to, are a pilot input, any remote piloting device, fleet manager, a simulation device, or the like. In this disclosure, “command” is any element of information and/or data that indicates a purposeful change of state of a control element, flight component, or actuator. A “purposeful change” is an efficient change to the flight path Command datum for input device and other inputs to this system may include pilot manipulations of physical control interfaces, remote signals generated from electronic devices, voice commands, physiological readings like eye movements, pedal manipulation, or a combination thereof, to name a few. A command datum may indicate a desired change in aircraft heading or thrust. According to some embodiments, a command datum may be derived from the input device, for example without limitation by performing a control algorithm. That is to say command datum is derived from a pilot input, for example without limitation in a form of moving an inceptor stick; and the command datum may be received by at least an actuator that in turn, actuates according to the command datum, for instance thereby moving at least a portion of aircraft, to accomplish the pilot's desired maneuver.

Furthermore, in system 300, the flight controller 116 is further configured to receive at least the command from the input device and generate a propulsion output as a function of the updated health datum 308. As used in this disclosure, “propulsion output” is any element of data sent to the propulsors in the plurality of flight components telling them what to do. For example, but without limitation, examples of propulsion outputs could be to initiate rotation of the propulsors, to stop rotation of the propulsors, to change the speed of the propulsors, and the like. Furthermore, the propulsion output is generated as a function of at least a battery metric receives from the sensor from computing device 120. Additionally, the flight controller 116 transmits the propulsion output to each rotor component of the plurality of flight components 112. The plurality of flight components then perform the command sent to them.

Now referring to FIG. 4 , an exemplary embodiment 400 of a flight controller 116 is illustrated. As used in this disclosure a “flight controller” is a computing device of a plurality of computing devices dedicated to data storage, security, distribution of traffic for load balancing, and flight instruction. Flight controller 116 may include and/or communicate with any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Further, flight controller 116 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. In embodiments, flight controller 116 may be installed in an aircraft, may control aircraft remotely, and/or may include an element installed in the aircraft and a remote element in communication therewith.

In an embodiment, and still referring to FIG. 4 , flight controller 116 may include a signal transformation component 404. As used in this disclosure a “signal transformation component” is a component that transforms and/or converts a first signal to a second signal, wherein a signal may include one or more digital and/or analog signals. For example, and without limitation, signal transformation component 404 may be configured to perform one or more operations such as preprocessing, lexical analysis, parsing, semantic analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component 404 may include one or more analog-to-digital convertors that transform a first signal of an analog signal to a second signal of a digital signal. For example, and without limitation, an analog-to-digital converter may convert an analog input signal to a 10-bit binary digital representation of that signal. In another embodiment, signal transformation component 404 may include transforming one or more low-level languages such as, but not limited to, machine languages and/or assembly languages. For example, and without limitation, signal transformation component 404 may include transforming a binary language signal to an assembly language signal. In an embodiment, and without limitation, signal transformation component 404 may include transforming one or more high-level languages and/or formal languages such as but not limited to alphabets, strings, and/or languages. For example, and without limitation, high-level languages may include one or more system languages, scripting languages, domain-specific languages, visual languages, esoteric languages, and the like thereof. As a further non-limiting example, high-level languages may include one or more algebraic formula languages, business data languages, string and list languages, object-oriented languages, and the like thereof.

Still referring to FIG. 4 , signal transformation component 404 may be configured to optimize an intermediate representation 408. As used in this disclosure an “intermediate representation” is a data structure and/or code that represents the input signal. Signal transformation component 404 may optimize intermediate representation as a function of a data-flow analysis, dependence analysis, alias analysis, pointer analysis, escape analysis, and the like thereof. In an embodiment, and without limitation, signal transformation component 404 may optimize intermediate representation 408 as a function of one or more inline expansions, dead code eliminations, constant propagation, loop transformations, and/or automatic parallelization functions. In another embodiment, signal transformation component 404 may optimize intermediate representation as a function of a machine dependent optimization such as a peephole optimization, wherein a peephole optimization may rewrite short sequences of code into more efficient sequences of code. Signal transformation component 404 may optimize intermediate representation to generate an output language, wherein an “output language,” as used herein, is the native machine language of flight controller 116. For example, and without limitation, native machine language may include one or more binary and/or numerical languages.

In an embodiment, and without limitation, signal transformation component 404 may include transform one or more inputs and outputs as a function of an error correction code. An error correction code, also known as error correcting code (ECC), is an encoding of a message or lot of data using redundant information, permitting recovery of corrupted data. An ECC may include a block code, in which information is encoded on fixed-size packets and/or blocks of data elements such as symbols of predetermined size, bits, or the like. Reed-Solomon coding, in which message symbols within a symbol set having q symbols are encoded as coefficients of a polynomial of degree less than or equal to a natural number k, over a finite field F with q elements; strings so encoded have a minimum hamming distance of k+1, and permit correction of (q−k−1)/2 erroneous symbols. Block code may alternatively or additionally be implemented using Golay coding, also known as binary Golay coding, Bose-Chaudhuri, Hocquenghem (BCH) coding, multidimensional parity-check coding, and/or Hamming codes. An ECC may alternatively or additionally be based on a convolutional code.

In an embodiment, and still referring to FIG. 4 , flight controller 116 may include a reconfigurable hardware platform 412. A “reconfigurable hardware platform,” as used herein, is a component and/or unit of hardware that may be reprogrammed, such that, for instance, a data path between elements such as logic gates or other digital circuit elements may be modified to change an algorithm, state, logical sequence, or the like of the component and/or unit. This may be accomplished with such flexible high-speed computing fabrics as field-programmable gate arrays (FPGAs), which may include a grid of interconnected logic gates, connections between which may be severed and/or restored to program in modified logic. Reconfigurable hardware platform 412 may be reconfigured to enact any algorithm and/or algorithm selection process received from another computing device and/or created using machine-learning processes.

Still referring to FIG. 4 , reconfigurable hardware platform 412 may include a logic component 416. As used in this disclosure a “logic component” is a component that executes instructions on output language. For example, and without limitation, logic component may perform basic arithmetic, logic, controlling, input/output operations, and the like thereof. Logic component 416 may include any suitable processor, such as without limitation a component incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; logic component 416 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Logic component 416 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating-point unit (FPU), and/or system on a chip (SoC). In an embodiment, logic component 416 may include one or more integrated circuit microprocessors, which may contain one or more central processing units, central processors, and/or main processors, on a single metal-oxide-semiconductor chip. Logic component 416 may be configured to execute a sequence of stored instructions to be performed on the output language and/or intermediate representation 408. Logic component 416 may be configured to fetch and/or retrieve instruction from a memory cache, wherein a “memory cache,” as used in this disclosure, is a stored instruction set on flight controller 116. Logic component 416 may be configured to decode instruction retrieved from memory cache to opcodes and/or operands. Logic component 416 may be configured to execute instruction on intermediate representation 408 and/or output language. For example, and without limitation, logic component 416 may be configured to execute an addition operation on intermediate representation 408 and/or output language.

In an embodiment, and without limitation, logic component 416 may be configured to calculate a flight element 420. As used in this disclosure a “flight element” is an element of datum denoting a relative status of aircraft. For example, and without limitation, flight element 420 may denote one or more torques, thrusts, airspeed velocities, forces, altitudes, groundspeed velocities, directions during flight, directions facing, forces, orientations, and the like thereof. For example, and without limitation, flight element 420 may denote that aircraft is cruising at an altitude and/or with a sufficient magnitude of forward thrust. As a further non-limiting example, flight status may denote that is building thrust and/or groundspeed velocity in preparation for a takeoff. As a further non-limiting example, flight element 420 may denote that aircraft is following a flight path accurately and/or sufficiently.

Still referring to FIG. 4 , flight controller 116 may include a chipset component 424. As used in this disclosure a “chipset component” is a component that manages data flow. In an embodiment, and without limitation, chipset component 424 may include a northbridge data flow path, wherein northbridge dataflow path may manage data flow from logic component 416 to a high-speed device and/or component, such as a RAM, graphics controller, and the like thereof. In another embodiment, and without limitation, chipset component 424 may include a southbridge data flow path, wherein southbridge dataflow path may manage data flow from logic component 416 to lower-speed peripheral buses, such as a peripheral component interconnect (PCI), industry standard architecture (ICA), and the like thereof. In an embodiment, and without limitation, southbridge data flow path may include managing data flow between peripheral connections such as ethernet, USB, audio devices, and the like thereof. Additionally or alternatively, chipset component 424 may manage data flow between logic component 416, memory cache, and a flight component 108. As used in this disclosure a “flight component” is a portion of an aircraft that can be moved or adjusted to affect one or more flight elements. For example, flight component 108 may include a component used to affect aircrafts' roll and pitch which may comprise one or more ailerons. As a further example, flight component 108 may include a rudder to control yaw of an aircraft. In an embodiment, chipset component 424 may be configured to communicate with a plurality of flight components as a function of flight element 420. For example, and without limitation, chipset component 424 may transmit to an aircraft rotor to reduce torque of a first lift propulsor and increase the forward thrust produced by a pusher component to perform a flight maneuver.

In an embodiment, and still referring to FIG. 4 , flight controller 116 is configured generate an autonomous function. As used in this disclosure an “autonomous function” is a mode and/or function of flight controller 116 that controls aircraft automatically. For example, and without limitation, autonomous function may perform one or more aircraft maneuvers, take offs, landings, altitude adjustments, flight leveling adjustments, turns, climbs, and/or descents. As a further non-limiting example, autonomous function may adjust one or more airspeed velocities, thrusts, torques, and/or groundspeed velocities. As a further non-limiting example, autonomous function may perform one or more flight path corrections and/or flight path modifications as a function of flight element 420. In an embodiment, autonomous function may include one or more modes of autonomy such as, but not limited to, autonomous mode, semi-autonomous mode, and/or non-autonomous mode. As used in this disclosure “autonomous mode” is a mode that automatically adjusts and/or controls aircraft and/or the maneuvers of aircraft in its entirety. For example, autonomous mode may denote that flight controller 116 will adjust aircraft. As used in this disclosure a “semi-autonomous mode” is a mode that automatically adjusts and/or controls a portion and/or section of aircraft.

In an embodiment, and still referring to FIG. 4 , flight controller 116 generates autonomous function as a function of an autonomous machine-learning model. As used in this disclosure an “autonomous machine-learning model” is a machine-learning model to produce an autonomous function output given flight element 420 and pilot override 428 as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. As used in this disclosure a “pilot override switch” is an element of datum representing one or more functions a pilot does to claim flight control of aircraft 100. For example, pilot override 428 may denote that a pilot is gaining control and/or maneuvering ailerons, rudders and/or propulsors. In an embodiment, pilot override 428 must include an implicit signal and/or an explicit signal. For example, and without limitation, pilot override 428 may include an explicit signal, wherein pilot explicitly states desire for control. As a further non-limiting example, pilot override 428 may include an explicit signal directing flight controller 116 to control and/or maintain entire aircraft, and/or entire flight plan. As a further non-limiting example, pilot override 428 may include an implicit signal, wherein flight controller 116 detects a malfunction, torque alteration, flight path deviation, and the like thereof. In an embodiment, and without limitation, pilot override 428 may include one or more explicit signals to reduce torque, and/or one or more implicit signals that torque may be reduced due to reduction of airspeed velocity. In an embodiment, and without limitation, pilot override 428 may include one or more local and/or global signals. For example, and without limitation, pilot override 428 may include a local signal that is transmitted by a pilot and/or crew member. As a further non-limiting example, pilot override 428 may include a global signal that is transmitted by air traffic control and/or one or more remote users that are in communication with pilot of aircraft.

Still referring to FIG. 4 , autonomous machine-learning model may include one or more autonomous machine-learning processes such as supervised, unsupervised, or reinforcement machine-learning processes that flight controller 116 and/or a remote device may or may not use in the generation of autonomous function. As used in this disclosure “remote device” is an external device to flight controller 116. Additionally or alternatively, autonomous machine-learning model may include one or more autonomous machine-learning processes that a field-programmable gate array (FPGA) may or may not use in the generation of autonomous function. Autonomous machine-learning process may include, without limitation machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elastic net regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 4 , autonomous machine learning model may be trained as a function of autonomous training data, wherein autonomous training data may correlate a flight element, pilot signal, and/or simulation data to an autonomous function. For example, and without limitation, a flight element of an airspeed velocity, a pilot signal of limited and/or no control of propulsors, and a simulation data of required airspeed velocity to reach the destination may result in an autonomous function that may include a semi-autonomous mode to increase thrust of propulsors. Autonomous training data may be received as a function of user-entered valuations of flight elements, pilot signals, simulation data, and/or autonomous functions. Flight controller 116 may receive autonomous training data by receiving correlations of flight element, pilot signal, and/or simulation data to an autonomous function that were previously received and/or determined during a previous iteration of generation of autonomous function. Autonomous training data may be received by one or more remote devices and/or FPGAs that at least correlate a flight element, pilot override, and/or simulation data to an autonomous function. Autonomous training data may be received in the form of one or more user-entered correlations of a flight element, pilot override, and/or simulation data to an autonomous function.

Still referring to FIG. 4 , flight controller 116 may receive autonomous machine-learning model from a remote device and/or FPGA that utilizes one or more autonomous machine learning processes, wherein a remote device and an FPGA is described above in detail. For example, and without limitation, a remote device may include a computing device, external device, processor, FPGA, microprocessor, and the like thereof. Remote device and/or FPGA may perform autonomous machine-learning process using autonomous training data to generate autonomous function and transmit output to flight controller 116. Remote device and/or FPGA may transmit a signal, bit, datum, or parameter to flight controller 116 that at least relates to autonomous function. Additionally or alternatively, remote device and/or FPGA may provide an updated machine-learning model. For example, and without limitation, an updated machine-learning model may be comprised of a firmware update, a software update, an autonomous machine-learning process correction, and the like thereof. As a non-limiting example, a software update may incorporate a new simulation data that relates to a modified flight element. Additionally or alternatively, the updated machine learning model may be transmitted to remote device and/or FPGA, wherein remote device and/or FPGA may replace autonomous machine-learning model with updated machine-learning model and generate the autonomous function as a function of flight element, pilot override, and/or simulation data using the updated machine-learning model. Updated machine-learning model may be transmitted by remote device and/or FPGA and received by flight controller 116 as a software update, firmware update, or corrected autonomous machine-learning model. For example, and without limitation autonomous machine learning model may utilize a neural net machine-learning process, wherein updated machine-learning model may incorporate a gradient boosting machine-learning process.

Still referring to FIG. 4 , flight controller 116 may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Further, flight controller may communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for commutatively connecting a flight controller to one or more of a variety of networks, and one or more devices. Examples of a network interface device may include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network may include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. Network may include any network topology and can may employ a wired and/or a wireless mode of communication.

In an embodiment, and still referring to FIG. 4 , flight controller 116 may include, but is not limited to, for example, a cluster of flight controllers in a first location and a second flight controller or cluster of flight controllers in a second location. Flight controller 116 may include one or more flight controllers dedicated to data storage, security, distribution of traffic for load balancing, and the like. Flight controller 116 may be configured to distribute one or more computing tasks as described below across a plurality of flight controllers, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. For example, and without limitation, flight controller 116 may implement a control algorithm to distribute and/or command plurality of flight controllers. As used in this disclosure a “control algorithm” is a finite sequence of well-defined computer implementable instructions that may determine flight component of plurality of flight components to be adjusted. For example, and without limitation, control algorithm may include one or more algorithms that reduce and/or prevent aviation asymmetry. As a further non-limiting example, control algorithms may include one or more models generated as a function of a software including, but not limited to Simulink by MathWorks, Natick, Mass., USA. In an embodiment, and without limitation, control algorithm may be configured to generate an auto-code, wherein an “auto-code,” is used herein, is a code and/or algorithm that is generated as a function of one or more models and/or software's. In another embodiment, control algorithm may be configured to produce a segmented control algorithm. As used in this disclosure a “segmented control algorithm” is control algorithm that has been separated and/or parsed into discrete sections. For example, and without limitation, segmented control algorithm may parse control algorithm into two or more segments, wherein each segment of control algorithm may be performed by one or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 4 , control algorithm may be configured to determine a segmentation boundary as a function of segmented control algorithm. As used in this disclosure a “segmentation boundary” is a limit and/or delineation associated with segments of the segmented control algorithm. For example, and without limitation, segmentation boundary may denote that a segment in the control algorithm has a first starting section and/or a first ending section. As a further non-limiting example, segmentation boundary may include one or more boundaries associated with an ability of flight component 108. In an embodiment, control algorithm may be configured to create an optimized signal communication as a function of segmentation boundary. For example, and without limitation, optimized signal communication may include identifying the discrete timing required to transmit and/or receive one or more segmentation boundaries. In an embodiment, and without limitation, creating optimized signal communication further comprises separating a plurality of signal codes across plurality of flight controllers. For example, and without limitation, plurality of flight controllers may include one or more formal networks, wherein formal networks transmit data along an authority chain and/or are limited to task-related communications. As a further non-limiting example, communication network may include informal networks, wherein informal networks transmit data in any direction. In an embodiment, and without limitation, plurality of flight controllers may include a chain path, wherein a “chain path,” as used herein, is a linear communication path comprising a hierarchy that data may flow through. In an embodiment, and without limitation, plurality of flight controllers may include an all-channel path, wherein an “all-channel path,” as used herein, is a communication path that is not restricted to a particular direction. For example, and without limitation, data may be transmitted upward, downward, laterally, and the like thereof. In an embodiment, and without limitation, plurality of flight controllers may include one or more neural networks that assign a weighted value to a transmitted datum. For example, and without limitation, a weighted value may be assigned as a function of one or more signals denoting that a flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 4 , plurality of flight controllers may include a master bus controller. As used in this disclosure a “master bus controller” is one or more devices and/or components that are connected to a bus to initiate a direct memory access transaction, wherein a bus is one or more terminals in a bus architecture. Master bus controller may communicate using synchronous and/or asynchronous bus control protocols. In an embodiment, master bus controller may include flight controller 116. In another embodiment, master bus controller may include one or more universal asynchronous receiver-transmitters (UART). For example, and without limitation, master bus controller may include one or more bus architectures that allow a bus to initiate a direct memory access transaction from one or more buses in the bus architectures. As a further non-limiting example, master bus controller may include one or more peripheral devices and/or components to communicate with another peripheral device and/or component and/or master bus controller. In an embodiment, master bus controller may be configured to perform bus arbitration. As used in this disclosure “bus arbitration” is method and/or scheme to prevent multiple buses from attempting to communicate with and/or connect to master bus controller. For example, and without limitation, bus arbitration may include one or more schemes such as a small computer interface system, wherein a small computer interface system is a set of standards for physical connecting and transferring data between peripheral devices and master bus controller by defining commands, protocols, electrical, optical, and/or logical interfaces. In an embodiment, master bus controller may receive intermediate representation 408 and/or output language from logic component 416, wherein output language may include one or more analog-to-digital conversions, low bit rate transmissions, message encryptions, digital signals, binary signals, logic signals, analog signals, and the like thereof described above in detail.

Still referring to FIG. 4 , master bus controller may communicate with a slave bus. As used in this disclosure a “slave bus” is one or more peripheral devices and/or components that initiate a bus transfer. For example, and without limitation, slave bus may receive one or more controls and/or asymmetric communications from master bus controller, wherein slave bus transfers data stored to master bus controller. In an embodiment, and without limitation, slave bus may include one or more internal buses, such as but not limited to a/an internal data bus, memory bus, system bus, front-side bus, and the like thereof. In another embodiment, and without limitation, slave bus may include one or more external buses such as external flight controllers, external computers, remote devices, printers, aircraft computer systems, flight control systems, and the like thereof.

In an embodiment, and still referring to FIG. 4 , control algorithm may optimize signal communication as a function of determining one or more discrete timings. For example, and without limitation master bus controller may synchronize timing of the segmented control algorithm by injecting high priority timing signals on a bus of master bus control. As used in this disclosure a “high priority timing signal” is information denoting that the information is important. For example, and without limitation, high priority timing signal may denote that a section of control algorithm is of high priority and should be analyzed and/or transmitted prior to any other sections being analyzed and/or transmitted. In an embodiment, high priority timing signal may include one or more priority packets. As used in this disclosure a “priority packet” is a formatted unit of data that is communicated between plurality of flight controllers. For example, and without limitation, priority packet may denote that a section of control algorithm should be used and/or is of greater priority than other sections.

Still referring to FIG. 4 , flight controller 116 may also be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of aircraft and/or computing device. Flight controller 116 may include a distributer flight controller. As used in this disclosure a “distributer flight controller” is a component that adjusts and/or controls a plurality of flight components as a function of a plurality of flight controllers. For example, distributer flight controller may include a flight controller that communicates with a plurality of additional flight controllers and/or clusters of flight controllers. In an embodiment, distributed flight control may include one or more neural networks. For example, neural network also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust connections and weights between nodes in adjacent layers of neural network to produce the desired values at output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 4 , a node may include, without limitation a plurality of inputs x_(i) that may receive numerical values from inputs to a neural network containing node and/or from other nodes. Node may perform a weighted sum of inputs using weights w_(i) that are multiplied by respective inputs x_(i). Additionally or alternatively, a bias b may be added to the weighted sum of inputs such that an offset is added to each unit in neural network layer that is independent of input to the layer. Weighted sum may then be input into a function φ, which may generate one or more outputs y. Weight w_(i) applied to an input x_(i) may indicate whether input is “excitatory,” indicating that it has strong influence on one or more outputs y, for instance by the corresponding weight having a large numerical value, and/or a “inhibitory,” indicating it has a weak effect influence on one more inputs y, for instance by the corresponding weight having a small numerical value. Values of weights w_(i) may be determined by training a neural network using training data, which may be performed using any suitable process as described above. In an embodiment, and without limitation, a neural network may receive semantic units as inputs and output vectors representing such semantic units according to weights w_(i) that are derived using machine-learning processes as described in this disclosure.

Still referring to FIG. 4 , flight controller 116 may include a sub-controller 432. As used in this disclosure a “sub-controller” is a controller and/or component that is part of a distributed controller as described above; for instance, flight controller 116 may be and/or include a distributed flight controller made up of one or more sub-controllers. For example, and without limitation, sub-controller 432 may include any controllers and/or components thereof that are similar to distributed flight controller and/or flight controller as described above. Sub-controller 432 may include any component of any flight controller as described above. Sub-controller 432 may be implemented in any manner suitable for implementation of a flight controller as described above. As a further non-limiting example, sub-controller 432 may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data across distributed flight controller as described above. As a further non-limiting example, sub-controller 432 may include a controller that receives a signal from a first flight controller and/or first distributed flight controller component and transmits signal to a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 4 , flight controller may include a co-controller 436. As used in this disclosure a “co-controller” is a controller and/or component that joins flight controller 116 as components and/or nodes of a distributer flight controller as described above. For example, and without limitation, co-controller 436 may include one or more controllers and/or components that are similar to flight controller 116. As a further non-limiting example, co-controller 436 may include any controller and/or component that joins flight controller 116 to distributer flight controller. As a further non-limiting example, co-controller 436 may include one or more processors, logic components and/or computing devices capable of receiving, processing, and/or transmitting data to and/or from flight controller 116 to distributed flight control system. Co-controller 436 may include any component of any flight controller as described above. Co-controller 436 may be implemented in any manner suitable for implementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 4 , flight controller 116 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, flight controller 116 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Flight controller may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.

Referring now to FIG. 5 , an exemplary embodiment of a machine-learning module 500 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 504 to generate an algorithm that will be performed by a computing device/module to produce outputs 508 given data provided as inputs 512; this is in contrast to a non-machine learning software program where commands to be executed are determined in advance by a user and written in a programming language.

Still referring to FIG. 5 , “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 504 may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 504 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 504 according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data 504 may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data 504 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 504 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 504 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5 , training data 504 may include one or more elements that are not categorized; that is, training data 504 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data 504 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 504 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 504 used by machine-learning module 500 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example flight elements and/or pilot signals may be inputs, wherein an output is an autonomous function.

Further referring to FIG. 5 , training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier % 112. Training data classifier % 112 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module 500 may generate a classifier using a classification algorithm, defined as a process whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 504. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 412 may classify elements of training data to sub-categories of flight elements such as torques, forces, thrusts, directions, and the like thereof.

Still referring to FIG. 5 , machine-learning module 500 may be configured to perform a lazy-learning process 512 and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining input and training set to derive the algorithm to be used to produce output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 504. Heuristic may include selecting some number of highest-ranking associations and/or training data 504 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing entirety of this disclosure, will be aware of various lazy-learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below.

Alternatively or additionally, and with continued reference to FIG. 5 , machine-learning processes as described in this disclosure may be used to generate machine-learning models 524. A “machine-learning model,” as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model 524 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model 524 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via process of “training” the network, in which elements from a training data 504 set are applied to input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust connections and weights between nodes in adjacent layers of neural network to produce desired values at output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 5 , machine-learning algorithms may include at least a supervised machine-learning process 528. At least a supervised machine-learning process 528, as defined herein, may include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include flight elements and/or pilot signals as described above as inputs, autonomous functions as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 504. Persons skilled in the art, upon reviewing entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process 528 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above.

Further referring to FIG. 5 , machine learning processes may include at least an unsupervised machine-learning processes 532. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like.

Still referring to FIG. 5 , machine-learning module 500 may be designed and configured to create a machine-learning model 524 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized may include the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 5 , machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes.

Now referring to FIG. 6 , an exemplary embodiment of method 600 for actuator monitoring of an electric aircraft is illustrated. The electric aircraft may include, but without limitation, any of the aircraft as disclosed herein and escribed above with reference to at least FIG. 1 . Aircraft may include an electric takeoff and vertical landing (eVTOL) aircraft in some embodiments.

Still referring to FIG. 6 , at step 605, method 600 includes coupling a plurality of flight components 112 to electric aircraft 100. The system further comprises a plurality of batteries and where in each battery of the plurality of batteries includes a sensor. The plurality of flight components may be any one of the flight components as disclosed herein and described above with reference to at least FIGS. 2-3 .

Still referring to FIG. 6 , at step 610, method 600 includes commutatively coupling a computing device 120 to each flight component 112. The computing device 120 may be any one of the computing devices as disclosed herein and described above with reference to at least FIGS. 2-3 and may include a computer, tablet, smartphone, or the like. The plurality of flight components 112 may be any one of the flight components as disclosed herein and described above with reference to at least FIGS. 2-3 .

Still referring to FIG. 6 , at step 615, method 600 includes detecting, at a computing device 120, an input health datum 304 associated with each rotor component 112. The input health datum 304 is configured to include a plurality of battery temperatures. The input health datum may also include rotational speed, missing parts, age, efficiency, voltage, or the like. The sensor attached to the computing device is configured to detect and transmit a health datum, such as a battery metric, to a flight controller. The computing device 120 may be any one of the computing devices as disclosed herein and described above with reference to at least FIGS. 2-3 . The input health datum 204 may be any of the health datums as disclosed herein and described above with reference to FIG. 2 . The plurality of flight components may be any one of the flight components as disclosed herein and described above with reference to at least FIGS. 2-3 .

Still referring to FIG. 6 , at step 620, method 600 includes transmitting, at a computing device, the input health datum 304 to a flight controller 116. The computing device 120 may be any one of the computing devices as disclosed herein and described above with reference to at least FIGS. 2-3 . The input health datum 304 may be any of the health datums as disclosed herein and described above with reference to FIG. 2 . The flight controller 116 may be any of the flight controllers as disclosed herein and described above with reference to FIGS. 2-4 .

Still referring to FIG. 6 , at step 625, method 600 includes receiving, at a flight controller, the input health datum 304 from the computing device 120. The flight controller 116 may be any of the flight controllers as disclosed herein and described above with reference to FIGS. 2-4 . The input health datum 304 may be any of the health datums as disclosed herein and described above with reference to FIG. 2 . The computing device 120 may be any one of the computing devices as disclosed herein and described above with reference to at least FIGS. 2-3 .

Still referring to FIG. 6 , at step 630, method 600 includes generating, at a flight controller, an updated health datum 308 as a function of the input health datum 304. The updated health datum is configured to include a plurality of battery temperatures in reference to a threshold temperature. The updated health datum may also include the same examples as the input health datum, accept closer to the threshold. The flight controller 116 may be any of the flight controllers as disclosed herein and described above with reference to FIGS. 2-4 . The updated health datum 304 may be any of the health datums as disclosed herein and described above with reference to FIG. 2 .

Still referring to FIG. 6 , at step 635, method 600 includes transmitting, at a flight controller, the updated health datum 304 to each of the rotors components in the plurality of flight components 112. The system is further configured to detect at least a command from the input device, transmit the command to a flight controller, and generate a propulsion output as a function of the command and battery metrics. The flight controller 116 may be any of the flight controllers as disclosed herein and described above with reference to FIGS. 2-4 . The updated health datum 304 may be any of the health datums as disclosed herein and described above with reference to FIG. 2 . The input health datum 304 may be any of the health datums as disclosed herein and described above with reference to FIG. 2 .

It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of software and/or software module.

Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a machine-readable storage medium may include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random-access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, may include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied in a data carrier in which signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein.

Examples of a computing device may include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of a computing device in exemplary form of a computer system 700 within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system 700 may include a processor 704 and a memory 708 that communicate with each other, and with other components, via a bus 428432. Bus 428432 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures.

Processor 704 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 704 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 704 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating-point unit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system 716 (BIOS), including basic routines that help to transfer information between elements within computer system 700, such as during start-up, may be stored in memory 708. Memory 708 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 720 embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory 708 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.

Computer system 700 may also include a storage device 724. Examples of a storage device (e.g., storage device 724) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 724 may be connected to bus 428432 by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 724 (or one or more components thereof) may be removably interfaced with computer system 700 (e.g., via an external port connector (not shown 208. Particularly, storage device 724 and an associated machine-readable medium 728 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 700. In one example, software 720 may reside, completely or partially, within machine-readable medium 728. In another example, software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In one example, a user of computer system 700 may enter commands and/or other information into computer system 700 via input device 732. Examples of an input device 732 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 732 may be interfaced to bus 428432 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 428432, and any combinations thereof. Input device 732 may include a touch screen interface that may be a part of or separate from display 736, discussed further below. Input device 732 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system 700 via storage device 724 (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device 740. A network interface device, such as network interface device 740, may be utilized for connecting computer system 700 to one or more of a variety of networks, such as network 744, and one or more remote devices 748 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 744, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 720, etc.) may be communicated to and/or from computer system 700 via network interface device 740.

Computer system 700 may further include a video display adapter 752 for communicating a displayable image to a display device, such as display device 736. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 752 and display device 736 may be utilized in combination with processor 708 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 700 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 428432 via a peripheral interface 756. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.

The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve systems and methods according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention. 

1. A system for actuator monitoring for an electric aircraft, the system comprising: a plurality of flight components coupled to the electric aircraft, wherein a flight component of the plurality of flight components includes a motor assembly comprising: a stator with a first magnetic element that generates a magnetic field; and a propulsor comprising: a second magnetic element that generates a magnetic field designed to interact with the first magnetic element; and a hub, rotatably mounted to the stator, affixed to the second magnetic element; a computing device communicatively coupled to each flight component of the plurality of flight components, wherein the computing device is configured to: detect an input health datum associated with each flight component of the plurality of flight components; and transmit the input health datum; and a flight controller communicatively connected to the computing devices, wherein the flight controller is configured to: receive the input health datum from the computing device; generate an updated health datum as a function of the input health datum, using a signal transformation component, wherein the signal transformation component transforms the input health datum into the updated health datum, and wherein the input health datum includes at least a battery temperature datum of at least one battery configured to power at least one flight component of the plurality of flight components; and transmit the updated health datum to each flight component of the plurality of flight components.
 2. (canceled)
 3. The system of claim 1, wherein the updated health datum includes a plurality of battery temperatures in reference to a threshold temperature.
 4. The system of claim 1, wherein the system further includes an input device, wherein the input device is configured to: detect at least a command; and transmit the at least a command to the flight controller.
 5. The system of claim 4, wherein the flight controller is further configured to: receive the at least a command from the input device; generate at least a propulsion output as a function of the updated health datum and the at least a command; and transmit the at least a propulsion output to each flight component of the plurality of flight components.
 6. The system of claim 1, wherein the system further comprises a plurality of batteries, and wherein each battery of the plurality of batteries includes a sensor.
 7. The system of claim 6, wherein the sensor is configured to: detect at least a battery metric from each battery of the plurality of batteries; and transmit the at least a battery metric to the flight controller.
 8. The system of claim 7, wherein the flight controller is further configured to: generate the updated health datum as a function of the input health datum and the at least a battery metric; and transmit the updated health datum to each flight component of the plurality of flight components.
 9. The system of claim 1, wherein each flight component of the plurality of flight components further comprises a sensor, wherein the sensor is configured to: detect the input health datum; and transmit the input health datum to the computing device.
 10. The system of claim 1, wherein the input health datum comprises a temperature reading.
 11. A method for actuator monitoring of an electric aircraft, the method comprising: coupling a plurality of flight components to the electric aircraft, wherein a flight component of the plurality of flight components includes a motor assembly comprising: a stator with a first magnetic element that generates a magnetic field; and a propulsor comprising: a second magnetic element that generates a magnetic field designed to interact with the first magnetic element; and a hub, rotatably mounted to the stator, affixed to the second magnetic element; communicatively coupling a computing device to each flight component of the plurality of flight components; detecting, at the computing device, an input health datum associated with each flight component of the plurality of flight components; transmitting, at the computing device, the input health datum; receiving, at a flight controller, the input health datum from the computing device; generating, at the flight controller, an updated health datum as a function of the input health datum, using a signal transformation component, wherein the signal transformation component transforms the input health datum into the updated health datum, and wherein the input health datum includes at least a battery temperature datum of at least one battery configured to power at least one flight component of the plurality of flight components; and transmitting, at the flight controller, the updated health datum to each flight component of the plurality of flight components.
 12. (canceled)
 13. The method of claim 11, wherein the updated health datum includes a plurality of battery temperatures in reference to a threshold temperature.
 14. The method of claim 11, wherein the system further includes an input device, wherein the input device is configured to: detect at least a command; and transmit the at least a command to the flight controller.
 15. The method of claim 14, wherein the flight controller is further configured to: receive the at least a command from the input device; generate at least a propulsion output as a function of the updated health datum and the at least a command; and transmit the at least a propulsion output to each flight component of the plurality of flight components.
 16. The method of claim 11, wherein the system further comprises a plurality of batteries, and wherein each battery of the plurality of batteries includes a sensor.
 17. The method of claim 16, wherein the sensor is configured to: detect at least a battery metric from each battery of the plurality of batteries; and transmit the at least a battery metric to the flight controller.
 18. The method of claim 17, wherein the flight controller is further configured to: generate the updated health datum as a function of the input health datum and the at least a battery metric; and transmit the updated health datum to each flight component of the plurality of flight components.
 19. The method of claim 11, wherein each flight component of the plurality of flight components further comprises a sensor, wherein the sensor is configured to: detect the input health datum; and transmit the input health datum to the computing device.
 20. The method of claim 11, wherein the input health datum comprises a temperature reading. 